A new hybrid method for quick and accurate calculation of forest transportation distances
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Transportation is a key forest logistics component and is a large proportion of the overall cost. Often, the transportation cost is determined by contractual agreements and based on the loaded distance from a supply to a demand point. Many alternative routes provide different distances (e.g., shortest route, fastest route, minimum fuel consumption), but these distances are approximate in the contractual agreement; hence, there is a mismatch between approximated costs and actual pay. It is necessary to match supply with demand when planning to use optimization models, as these models must cover many supply and demand points. From this point, many distances need to be established. These can be generated dynamically before optimization or generated a priori as static distance tables. The former can take a long time, whereas the latter needs to use aggregated zones that remain static over time because supply points, such as harvest areas, change continually. To enable fast optimization, distances between zones and demand points can be precomputed; however, they represent only estimated distances between the actual supply point and the demand points. We propose a hybrid method to improve quality and estimate accurate distances in a quick process, using a large case study from a company in Sweden with a standardized system that directly uses computed reference distances as contractually agreed. Results show that many distance estimation approaches give poor cost estimates (1–20%) and increase transportation costs (0.2–0.6%).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it